Selective voting in convex-hull ensembles improves classification accuracy

摘要

ObjectiveClassification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routine clinical practice. They generally classify all patients according to the same criteria, under an implicit assumption of population homogeneity. The objective here is to allow for population heterogeneity, possibly unrecognized, in order to increase classification accuracy and further the goal of tailoring therapies on an individualized basis.